Context-aware Answer Selection in Community Question Answering Exploiting Spatial Temporal Bidirectional Long Short-Term Memory

Abstract

Community Question Answering (CQA) sites provide knowledge sharing facility as the users can post questions and other users can share their answers. The selection of top-quality answers from the set of answers in a thread is a significant and challenging task in Natural Language Processing (NLP). To address this issue, we propose a deep learning based spatial temporal Bidirectional Long Short-Term Memory (Bi-LSTM) algorithm. The existing studies mainly focus only computing semantic similarity between questions and answers using votes given by the users. The proposed hybrid approach, based on both forward and backward, consider question to answer and answer to answer similarity. The forward LSTM captures the spatial impact of the answer to estimate the relevancy, whereas the backward LSTM learns temporal features with the answer to predict the best quality answer. Moreover, spatial Bi-LSTM captures past and future dependencies for a better understanding of context and to improve the effectiveness of answer selection. For extracting meaningful information from noisy text data, data is preprocessed following standard steps such as tokenization, parsing, lemmatization, stop words removal, part of speech tagging and entities extraction. Word embeddings-based Paragraph to vector (par2vec) has additional input nodes to represent paragraph information in vector for context understanding. The empirical analysis carried out on the SemEval CQA dataset shows that the proposed model outperforms state-of-art answer selection approaches.